Research articles for the 2019-11-05
A Dynamic Model of Managerial Entrenchment and the Positive Incentives It Creates
SSRN
This paper presents a model of entrenchment in which a CEO chooses how much effort to put into boosting the firm's productivity and the board and CEO bargain over executive-compensation and investment policies. The surplus that bargaining allocates derives from the reduction in value of the firm's capital that occurs if the CEO is replaced. Even if the CEO has no ownership stake, she exerts effort in order to increase the value of the capital at risk. This increases the shared surplus, which increases the CEO's current pay. Investment increases the surplus to be shared, increasing the CEO's future pay.
SSRN
This paper presents a model of entrenchment in which a CEO chooses how much effort to put into boosting the firm's productivity and the board and CEO bargain over executive-compensation and investment policies. The surplus that bargaining allocates derives from the reduction in value of the firm's capital that occurs if the CEO is replaced. Even if the CEO has no ownership stake, she exerts effort in order to increase the value of the capital at risk. This increases the shared surplus, which increases the CEO's current pay. Investment increases the surplus to be shared, increasing the CEO's future pay.
A Regulated Market Under Sanctions: On Tail Dependence Between Oil, Gold, and Tehran Stock Exchange Index
arXiv
arXiv
We demonstrate that the tail dependence should always be taken into account as a proxy for systematic risk of loss for investments. We provide the clear statistical evidence of that the structure of investment portfolios on a regulated market should be adjusted to the price of gold. Our finding suggests that the active bartering of oil for goods would prevent collapsing the national market facing international sanctions.
A two-dimensional propensity score matching method for longitudinal quasi-experimental studies: A focus on travel behavior and the built environment
arXiv
arXiv
The lack of longitudinal studies of the relationship between the built environment and travel behavior has been widely discussed in the literature. This paper discusses how standard propensity score matching estimators can be extended to enable such studies by pairing observations across two dimensions: longitudinal and cross-sectional. Researchers mimic randomized controlled trials (RCTs) and match observations in both dimensions, to find synthetic control groups that are similar to the treatment group and to match subjects synthetically across before-treatment and after-treatment time periods. We call this a two-dimensional propensity score matching (2DPSM). This method demonstrates superior performance for estimating treatment effects based on Monte Carlo evidence. A near-term opportunity for such matching is identifying the impact of transportation infrastructure on travel behavior.
Bank Loan Loss Provisioning During Election Years in Nigeria
SSRN
The paper investigates the behavior of loan loss provisions during election years in Nigeria. Election events create uncertainties in the business environment in Nigeria which can increase the credit risk that banks face. The findings reveal that the banking sector had high loan loss provisions when it is under-capitalised during election years. However, the election year did not have a significant effect on the level of loan loss provisions in the Nigerian banking sector.
SSRN
The paper investigates the behavior of loan loss provisions during election years in Nigeria. Election events create uncertainties in the business environment in Nigeria which can increase the credit risk that banks face. The findings reveal that the banking sector had high loan loss provisions when it is under-capitalised during election years. However, the election year did not have a significant effect on the level of loan loss provisions in the Nigerian banking sector.
Beyond the Black Box: An Intuitive Approach to Investment Prediction with Machine Learning
SSRN
The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black boxâ€, demanding interpretation and additional explanation. In this paper, we present a framework for demystifying the behavior of machine learning models and decomposing their predictions into linear, nonlinear, and interaction components. We also show how to decompose a model’s predictive efficacy into these same components. Together, this analysis forms a “model fingerprint†which we use to summarize its key characteristics and illustrate its similarities and differences compared to other models. We present a case study of this approach applying random forest, gradient boosting machine, and neural network models to the challenge of predicting monthly currency returns. We find that all three models reliably identify intuitive effects in the currency market, and that they also find new relationships attributable to nonlinearities and variable interactions. We argue that an understanding of these predictive components may help astute investors generate superior risk-adjusted returns.
SSRN
The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black boxâ€, demanding interpretation and additional explanation. In this paper, we present a framework for demystifying the behavior of machine learning models and decomposing their predictions into linear, nonlinear, and interaction components. We also show how to decompose a model’s predictive efficacy into these same components. Together, this analysis forms a “model fingerprint†which we use to summarize its key characteristics and illustrate its similarities and differences compared to other models. We present a case study of this approach applying random forest, gradient boosting machine, and neural network models to the challenge of predicting monthly currency returns. We find that all three models reliably identify intuitive effects in the currency market, and that they also find new relationships attributable to nonlinearities and variable interactions. We argue that an understanding of these predictive components may help astute investors generate superior risk-adjusted returns.
Blockchain Finance: Questions Regulators Ask
SSRN
This article provides a discussion on some issues in blockchain finance that regulators are concerned about â€" an area which bitcoin promoters have remained silent about. Blockchain technology in finance has several benefits for financial intermediation in the financial system; notwithstanding, several issues persist which if addressed can make the adoption of blockchain technology in finance easier and accepted by regulators. The blockchain issues discussed in this article are relevant for recent debates in blockchain finance.
SSRN
This article provides a discussion on some issues in blockchain finance that regulators are concerned about â€" an area which bitcoin promoters have remained silent about. Blockchain technology in finance has several benefits for financial intermediation in the financial system; notwithstanding, several issues persist which if addressed can make the adoption of blockchain technology in finance easier and accepted by regulators. The blockchain issues discussed in this article are relevant for recent debates in blockchain finance.
Brief Remarks on the Necessary Clarification of Market Abuse Prohibitions in Times of Shareholder Activism
SSRN
Three years after implementation of the European Market Abuse Regulation (MAR), the time has come for the general review of its application and effectiveness. The extent and content of any amendments that may be proposed as a result of this review will naturally depend on the contributions of all involved stakeholders. Mobilisation by them, to provide comments based on their experience and analysis, is therefore critical, particularly in light of the first review documents published by European authorities. There seems indeed to be a striking gap between the expectations of practitioners and the concerns expressed by European and national authorities. While the latter show a certain degree of sensitivity to real issues, including bureaucratic overload resulting from specific requirements, the more fundamental issues with the MAR are barely mentioned. Most serious appear to be: its excessive breadth; the ever-increasing ambiguity of key elements and implicit or explicit policy choices, including the central notions of “use†and “inside informationâ€, resulting in an evasive definition of an offence which has reached the level of vagueness squared; the confusion between insider trading prohibitions and positive disclosure requirements imposed on listed issuers; and the general lack of legal certainty in its language and application, perhaps as a result of its approach to an admittedly worthy core ambition to ensure equal information among market participants. Key to success of MAR’s extensive approach to insider trading â€" in stark contrast to the US conception of the offence, which focuses on freedom to trade and is directed toward fiduciary duty violations â€" would seem to be safe harbours providing clear guidance to financial markets on what’s allowed. Unfortunately, the MAR fails to do so. In this regard, shareholder activism seems to represent an excellent test bed for various MAR provisions. It well reveals the structural weaknesses of European legislation and underlines the necessity for public normative authorities to reconcile proactively the MAR prohibitions ex ante, in accordance with existing general criminal law principles, and secure shareholder legitimate engagement, therefore minimising the risks and costs of unpredictability, arbitrariness and national divergence in the EU. The legal imperatives to do so here are in line with the economic and financial imperatives. The objective is not to take sides a priori but, if we adhere to the principle that the market as a whole would therefore function more effectively, to ensure that all market participants understand more clearly defined rules of conduct, enabling them and the markets to serve society in an optimally efficient and responsible way.
SSRN
Three years after implementation of the European Market Abuse Regulation (MAR), the time has come for the general review of its application and effectiveness. The extent and content of any amendments that may be proposed as a result of this review will naturally depend on the contributions of all involved stakeholders. Mobilisation by them, to provide comments based on their experience and analysis, is therefore critical, particularly in light of the first review documents published by European authorities. There seems indeed to be a striking gap between the expectations of practitioners and the concerns expressed by European and national authorities. While the latter show a certain degree of sensitivity to real issues, including bureaucratic overload resulting from specific requirements, the more fundamental issues with the MAR are barely mentioned. Most serious appear to be: its excessive breadth; the ever-increasing ambiguity of key elements and implicit or explicit policy choices, including the central notions of “use†and “inside informationâ€, resulting in an evasive definition of an offence which has reached the level of vagueness squared; the confusion between insider trading prohibitions and positive disclosure requirements imposed on listed issuers; and the general lack of legal certainty in its language and application, perhaps as a result of its approach to an admittedly worthy core ambition to ensure equal information among market participants. Key to success of MAR’s extensive approach to insider trading â€" in stark contrast to the US conception of the offence, which focuses on freedom to trade and is directed toward fiduciary duty violations â€" would seem to be safe harbours providing clear guidance to financial markets on what’s allowed. Unfortunately, the MAR fails to do so. In this regard, shareholder activism seems to represent an excellent test bed for various MAR provisions. It well reveals the structural weaknesses of European legislation and underlines the necessity for public normative authorities to reconcile proactively the MAR prohibitions ex ante, in accordance with existing general criminal law principles, and secure shareholder legitimate engagement, therefore minimising the risks and costs of unpredictability, arbitrariness and national divergence in the EU. The legal imperatives to do so here are in line with the economic and financial imperatives. The objective is not to take sides a priori but, if we adhere to the principle that the market as a whole would therefore function more effectively, to ensure that all market participants understand more clearly defined rules of conduct, enabling them and the markets to serve society in an optimally efficient and responsible way.
Closed-Form Multi-Factor Copula Models with Observation-Driven Dynamic Factor Loadings
SSRN
We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001â€"2014. The proposed multi-factor structure is much better than existing (single-factor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value or momentum.
SSRN
We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001â€"2014. The proposed multi-factor structure is much better than existing (single-factor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value or momentum.
Closed-Form Risk-Minimizing Hedge Ratios for Affine GARCH Models
SSRN
This paper presents closed-form expressions for risk-minimizing hedging strategies under affine GARCH models driven by Gaussian innovations. Our expressions are applicable to European derivatives with payoff functions that admit an inverse Laplace transform representation, such as calls and puts. The solutions are derived under risk-neutral dynamics based on a variance-dependent pricing kernel that incorporates market prices of both equity and variance risks. The hedging strategy that is obtained is locally risk-minimizing (LRM) under the risk-neutral measure and also variance-optimal in the sense that it minimizes the expected value of the squared terminal hedging error. In addition, a first-order approximation of the hedge ratio is provided, and the continuous-time LRM strategy based on the weak limit of the underlying GARCH model is derived. Several numerical experiments are conducted to compare our analytic formulas to Monte Carlo estimators, to investigate the accuracy of the proposed LRM approximations, and to test the numerical convergence of the GARCH-based LRM hedge ratio to its continuous-time counterpart.
SSRN
This paper presents closed-form expressions for risk-minimizing hedging strategies under affine GARCH models driven by Gaussian innovations. Our expressions are applicable to European derivatives with payoff functions that admit an inverse Laplace transform representation, such as calls and puts. The solutions are derived under risk-neutral dynamics based on a variance-dependent pricing kernel that incorporates market prices of both equity and variance risks. The hedging strategy that is obtained is locally risk-minimizing (LRM) under the risk-neutral measure and also variance-optimal in the sense that it minimizes the expected value of the squared terminal hedging error. In addition, a first-order approximation of the hedge ratio is provided, and the continuous-time LRM strategy based on the weak limit of the underlying GARCH model is derived. Several numerical experiments are conducted to compare our analytic formulas to Monte Carlo estimators, to investigate the accuracy of the proposed LRM approximations, and to test the numerical convergence of the GARCH-based LRM hedge ratio to its continuous-time counterpart.
Customer Concentration and Corporate Real Estate Holdings
SSRN
We present new empirical evidence that higher customer concentration leads to lower corporate real estate holdings at the supplier firm level. Further evidence shows that this effect is causal and more pronounced when the likelihood/impact of losing primary customers is higher or when suppliers have less bargaining power. Finally, we show that firms with a concentrated customer base tend to choose capitalized leasing in lieu of holding real estate.
SSRN
We present new empirical evidence that higher customer concentration leads to lower corporate real estate holdings at the supplier firm level. Further evidence shows that this effect is causal and more pronounced when the likelihood/impact of losing primary customers is higher or when suppliers have less bargaining power. Finally, we show that firms with a concentrated customer base tend to choose capitalized leasing in lieu of holding real estate.
Deep Hedging: Learning to Simulate Equity Option Markets
arXiv
arXiv
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.
Engel's law in the commodity composition of exports
arXiv
arXiv
Different shares of distinct commodity sectors in production, trade, and consumption illustrate how resources and capital are allocated and invested. Economic progress has been claimed to change the share distribution in a universal manner as exemplified by the Engel's law for the household expenditure and the shift from primary to manufacturing and service sector in the three sector model. Searching for large-scale quantitative evidence of such correlation, we analyze the gross-domestic product (GDP) and international trade data based on the standard international trade classification (SITC) in the period 1962 to 2000. Three categories, among ten in the SITC, are found to have their export shares significantly correlated with the GDP over countries and time; The machinery category has positive and food and crude materials have negative correlations. The export shares of commodity categories of a country are related to its GDP by a power-law with the exponents characterizing the GDP-elasticity of their export shares. The distance between two countries in terms of their export portfolios is measured to identify several clusters of countries sharing similar portfolios in 1962 and 2000. We show that the countries whose GDP is increased significantly in the period are likely to transit to the clusters displaying large share of the machinery category.
Governance, Capital Flight and Industrialisation in Africa
SSRN
The study examines the role of governance in modulating the effect of capital flight on industrialisation in Africa. The empirical evidence is based on Generalised Method of Moments and governance is bundled by principal component analysis, namely: (i) political governance from political stability and “voice and accountabilityâ€; (ii) economic governance from government effectiveness and regulation quality; and (iii) institutional governance from corruption-control and the rule of law. First, governance increases industrialisation whereas capital flight has the opposite effect; and second, governance does not significantly mitigate the negative effect of capital flight on industrialisation. Policy implications are discussed.
SSRN
The study examines the role of governance in modulating the effect of capital flight on industrialisation in Africa. The empirical evidence is based on Generalised Method of Moments and governance is bundled by principal component analysis, namely: (i) political governance from political stability and “voice and accountabilityâ€; (ii) economic governance from government effectiveness and regulation quality; and (iii) institutional governance from corruption-control and the rule of law. First, governance increases industrialisation whereas capital flight has the opposite effect; and second, governance does not significantly mitigate the negative effect of capital flight on industrialisation. Policy implications are discussed.
Graph Theoretical Representations of Equity Indices and their Centrality Measures
SSRN
We define and study a correlation based notion of equity market centrality. This is pursued by examining the minimum spanning tree of a complete market graph that represents trading pattern similarities between the members of indices of equity securities. Specifically, we consider the degree centrality of the minimal spanning tree extracted from the complete market graph, and we compare and contrast this measure of centrality with the average pairwise correlation between members of an equity index. In addition, we perform numerical experiments on United States equity indices of varying size in addition to several major international equity markets. We highlight differences between the interpretation of this centrality measure for small and large market networks.
SSRN
We define and study a correlation based notion of equity market centrality. This is pursued by examining the minimum spanning tree of a complete market graph that represents trading pattern similarities between the members of indices of equity securities. Specifically, we consider the degree centrality of the minimal spanning tree extracted from the complete market graph, and we compare and contrast this measure of centrality with the average pairwise correlation between members of an equity index. In addition, we perform numerical experiments on United States equity indices of varying size in addition to several major international equity markets. We highlight differences between the interpretation of this centrality measure for small and large market networks.
Information Asymmetry and the Recent Financial Crisis
SSRN
The financial crisis of 2008 was preceded by smaller crises that also produced substantial levels of systemic risk. In addition to their impact on global markets, these lesser events shared several symptoms with the ones that later caused the Great Recession. If regulators and supervisors of the financial industry are to avoid future predicaments, they must monitor indicators beyond the traditional economic parameters. The goal of this paper is to discuss factors that prevented financial regulators from acting on deficiencies found in the financial sector before and during the most recent global financial crisis of 2008. In retrospect, we found that behavioral trends observed during the liquidation of Long-Term Capital Management, a massive hedge fund that failed in 1998 should have warned the regulators about potential risks that were clearly detected in 2008. The inquiry questions on this paper are answered through the analyses of three cases, Long-Term characterizing the crisis of 1998, and Bear Stearns and Lehman Brothers, investment banks that epitomized the global crisis of 2008. The result of this investigative study shows that several behaviors portrayed by regulators and financial executives throughout the period enabled the chain of events that culminated with the global crisis. This paper analyzes one of them: information asymmetry.
SSRN
The financial crisis of 2008 was preceded by smaller crises that also produced substantial levels of systemic risk. In addition to their impact on global markets, these lesser events shared several symptoms with the ones that later caused the Great Recession. If regulators and supervisors of the financial industry are to avoid future predicaments, they must monitor indicators beyond the traditional economic parameters. The goal of this paper is to discuss factors that prevented financial regulators from acting on deficiencies found in the financial sector before and during the most recent global financial crisis of 2008. In retrospect, we found that behavioral trends observed during the liquidation of Long-Term Capital Management, a massive hedge fund that failed in 1998 should have warned the regulators about potential risks that were clearly detected in 2008. The inquiry questions on this paper are answered through the analyses of three cases, Long-Term characterizing the crisis of 1998, and Bear Stearns and Lehman Brothers, investment banks that epitomized the global crisis of 2008. The result of this investigative study shows that several behaviors portrayed by regulators and financial executives throughout the period enabled the chain of events that culminated with the global crisis. This paper analyzes one of them: information asymmetry.
Integration of ESG in Asset Allocation
SSRN
In this paper, we try to identify the relationship between the ESG scores and stocks' performance and risk measures. Using the ESG database of MSCI, we split the global investment universe into three regions: Europe, North America and Asia-Pacic. The investment universe of each region is dened by the components of the corresponding MSCI index which is rebalanced every month. A simple statistical check allows us to verify the integrity of the database. For the portfolio construction of E/S/G/ESG factors, the ESG scores are normalized for all stocks in the same sector such that the sectorial bias is minimized. We nd that Governance score can signicantly improve the portfolio's performance both in Europe and North America, meanwhile, the market of Asia-Pacic is not sensitive to E/S/G/ESG scores. It is interesting to remark that the Governance factor in Europe can be explained by traditional Quality factor, meaning that there is some equivalence between Governance score and Quality indicators. In terms of risk measures, we observe that stocks with higher Governance and Environmental scores are exposed to lower risks measured by maximum drawdown or volatility, for Europe and North America. In Asia-Pacic, it is difficult to make any conclusion on the relationship between risk measures and ESG scores.
SSRN
In this paper, we try to identify the relationship between the ESG scores and stocks' performance and risk measures. Using the ESG database of MSCI, we split the global investment universe into three regions: Europe, North America and Asia-Pacic. The investment universe of each region is dened by the components of the corresponding MSCI index which is rebalanced every month. A simple statistical check allows us to verify the integrity of the database. For the portfolio construction of E/S/G/ESG factors, the ESG scores are normalized for all stocks in the same sector such that the sectorial bias is minimized. We nd that Governance score can signicantly improve the portfolio's performance both in Europe and North America, meanwhile, the market of Asia-Pacic is not sensitive to E/S/G/ESG scores. It is interesting to remark that the Governance factor in Europe can be explained by traditional Quality factor, meaning that there is some equivalence between Governance score and Quality indicators. In terms of risk measures, we observe that stocks with higher Governance and Environmental scores are exposed to lower risks measured by maximum drawdown or volatility, for Europe and North America. In Asia-Pacic, it is difficult to make any conclusion on the relationship between risk measures and ESG scores.
Intensity estimation of transaction arrivals in the intraday electricity market
arXiv
arXiv
In the following paper, we examine the problem of the intensity estimation of transaction arrivals in the intraday electricity markets. Assuming the inter-arrival times distribution and considering multiple intensity function types, we utilize a maximum likelihood estimation.
A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models. We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the $\mathcal{L}^1$ and $\mathcal{L}^2$ norms of the difference between their counting processes and the realized one. The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market.
Küresel Faktörlerden Uluslararası Hisse Senedi Piyasalarına Volatilite Yayılma Etkileri (Volatility Spillover Effects From Global Factors to International Stock Markets)
SSRN
Turkish Abstract: Bu çalışmanın amacı, son 10 yıldır değişmekte olan uluslararası finansal yapının temel dinamiklerinin, hisse senedi piyasalarının volatilitesi üzerindeki etkisini belirlemektir. Bu amaçla emtia fiyatları, yatırımcıların risk iştahı, küresel ticaret hacmi ve ABD'nin getiri eğrisi eğimi küresel faktörler olarak belirlenmiştir. İncelenen borsa endeksleri için volatilite yayılma etkisinin, risk iştahı endeksi (VIX), ABD getiri farkı (T10Y3M) ve petrol volatilite endeksi (OVX) küresel değişkenlerinden kaynaklandığı tespit edilmiştir. Ayrıca bu etkinin gelişmiş piyasalarda daha büyük olduğu belirlenmiştir. Küresel ticaretin borsalar üzerindeki etkisi ise sınırlıdır. Bu sonuçlar portföy yöneticileri ve politika yapıcılar için küresel faktörlerin borsalar üzerindeki etkilerini ve borsa getirisi oynaklığını tahmin etmede oldukça önemlidir.English Abstract: The aim of this study is to determine the volatility effect of the fundamental dynamics of the international financial structure, which has been changing for the last 10 years, on the stock markets. For this purpose, global trading volume, US' yield spread, commodity prices and risk appetite of investors variables are determined as the global factors. It is found that the spillover effect for stock markets indexes are caused by Volatility Index (VIX), US’ yield spreads, and Oil Volatility Index (OVX). Also,this effect is greater stock markets indexes of developed countries. The impact of global trade on stock exchanges is limited. These results are important for portfolio managers and policy makers in predicting the impact of global factors on stock exchanges and stock market return volatility
SSRN
Turkish Abstract: Bu çalışmanın amacı, son 10 yıldır değişmekte olan uluslararası finansal yapının temel dinamiklerinin, hisse senedi piyasalarının volatilitesi üzerindeki etkisini belirlemektir. Bu amaçla emtia fiyatları, yatırımcıların risk iştahı, küresel ticaret hacmi ve ABD'nin getiri eğrisi eğimi küresel faktörler olarak belirlenmiştir. İncelenen borsa endeksleri için volatilite yayılma etkisinin, risk iştahı endeksi (VIX), ABD getiri farkı (T10Y3M) ve petrol volatilite endeksi (OVX) küresel değişkenlerinden kaynaklandığı tespit edilmiştir. Ayrıca bu etkinin gelişmiş piyasalarda daha büyük olduğu belirlenmiştir. Küresel ticaretin borsalar üzerindeki etkisi ise sınırlıdır. Bu sonuçlar portföy yöneticileri ve politika yapıcılar için küresel faktörlerin borsalar üzerindeki etkilerini ve borsa getirisi oynaklığını tahmin etmede oldukça önemlidir.English Abstract: The aim of this study is to determine the volatility effect of the fundamental dynamics of the international financial structure, which has been changing for the last 10 years, on the stock markets. For this purpose, global trading volume, US' yield spread, commodity prices and risk appetite of investors variables are determined as the global factors. It is found that the spillover effect for stock markets indexes are caused by Volatility Index (VIX), US’ yield spreads, and Oil Volatility Index (OVX). Also,this effect is greater stock markets indexes of developed countries. The impact of global trade on stock exchanges is limited. These results are important for portfolio managers and policy makers in predicting the impact of global factors on stock exchanges and stock market return volatility
Optimal Reinsurance with Multiple Reinsurers: Distortion Risk Measures, Distortion Premium Principles, and Heterogeneous Beliefs
SSRN
This paper unifies the work on multiple reinsurers, distortion risk measures, premium budgets, and heterogeneous beliefs. An insurer minimizes a distortion risk measure, while seeking reinsurance with finitely many reinsurers. The reinsurers use distortion premium principles, and they are allowed to have heterogeneous beliefs regarding the underlying probability distribution. We provide a characterization of optimal reinsurance indemnities, and we show that they are of a layer-insurance type. This is done both with and without a budget constraint, i.e., an upper bound constraint on the aggregate premium. Moreover, the optimal reinsurance indemnities enable us to identify a representative reinsurer in both situations. Finally, two examples with the Conditional Value-at-Risk illustrate our results.
SSRN
This paper unifies the work on multiple reinsurers, distortion risk measures, premium budgets, and heterogeneous beliefs. An insurer minimizes a distortion risk measure, while seeking reinsurance with finitely many reinsurers. The reinsurers use distortion premium principles, and they are allowed to have heterogeneous beliefs regarding the underlying probability distribution. We provide a characterization of optimal reinsurance indemnities, and we show that they are of a layer-insurance type. This is done both with and without a budget constraint, i.e., an upper bound constraint on the aggregate premium. Moreover, the optimal reinsurance indemnities enable us to identify a representative reinsurer in both situations. Finally, two examples with the Conditional Value-at-Risk illustrate our results.
Overnight Momentum, Informational Shocks, and Late-Informed Trading in China
SSRN
Based on high-frequency firm-level data, this paper uncovers new empirical patterns on intraday momentum in China. First, there exists a strong intraday momentum effect at the firm level. Second, the intraday predictability stems mainly from the overnight component rather than the opening half-hour component, which is consistent with the microstructure features of the Chinese market. Third, the intraday predictability attenuates (strengthens) following large positive (negative) informational shocks, implying a striking asymmetric reaction by market participants. Finally, we document that late-informed traders are relatively less experienced or skillful. Overall, the empirical results lend support to the model of late-informed trading.
SSRN
Based on high-frequency firm-level data, this paper uncovers new empirical patterns on intraday momentum in China. First, there exists a strong intraday momentum effect at the firm level. Second, the intraday predictability stems mainly from the overnight component rather than the opening half-hour component, which is consistent with the microstructure features of the Chinese market. Third, the intraday predictability attenuates (strengthens) following large positive (negative) informational shocks, implying a striking asymmetric reaction by market participants. Finally, we document that late-informed traders are relatively less experienced or skillful. Overall, the empirical results lend support to the model of late-informed trading.
Owners of the World's Listed Companies
SSRN
The approximately 41 000 listed companies in the world have a combined market value of more than USD 80 trillion, equivalent to the global GDP. By using firm-level ownership information from the 10 000 largest listed companies, that together make up 90% of the global market capitalisation. We provide unique data about who their owners are and how they own. The findings provide an empirical starting point for understanding how important features in corporate ownership may impact key policy priorities such as productivity growth and business sector dynamics.
SSRN
The approximately 41 000 listed companies in the world have a combined market value of more than USD 80 trillion, equivalent to the global GDP. By using firm-level ownership information from the 10 000 largest listed companies, that together make up 90% of the global market capitalisation. We provide unique data about who their owners are and how they own. The findings provide an empirical starting point for understanding how important features in corporate ownership may impact key policy priorities such as productivity growth and business sector dynamics.
Product Innovation and Credit Market Disruptions
SSRN
We use detailed product- and firm-level data for the consumer goods industry to provide new evidence that credit market disruptions significantly affected the level, quality, and type of product innovation during the recent financial crisis. We employ two alternative strategies to measure credit market disruptions: (1) we measure geographic variation in the exposure of a county to lenders that significantly cut back their national supply of small business loans, and (2) we use preexisting firm-level variation in the need to raise external funds at a time when syndicated lending markets saw a significant contraction. Using detailed product- and firm-level barcode data, we find that credit market disruptions did not affect the rate of incremental innovation on a firm’s existing products but limited the expansion of their product portfolio to new modules and categories. Consistent with a credit frictions channel, these effects are concentrated in smaller, younger, and non-publicly listed firms. Our estimates further indicate that products introduced in new categories during the financial crisis by firms exposed to credit market frictions are less successful throughout their entire life cycle than products introduced in new categories by the same firm during normal times. Overall, our findings suggest that disrupted credit markets disrupt radical product innovation.
SSRN
We use detailed product- and firm-level data for the consumer goods industry to provide new evidence that credit market disruptions significantly affected the level, quality, and type of product innovation during the recent financial crisis. We employ two alternative strategies to measure credit market disruptions: (1) we measure geographic variation in the exposure of a county to lenders that significantly cut back their national supply of small business loans, and (2) we use preexisting firm-level variation in the need to raise external funds at a time when syndicated lending markets saw a significant contraction. Using detailed product- and firm-level barcode data, we find that credit market disruptions did not affect the rate of incremental innovation on a firm’s existing products but limited the expansion of their product portfolio to new modules and categories. Consistent with a credit frictions channel, these effects are concentrated in smaller, younger, and non-publicly listed firms. Our estimates further indicate that products introduced in new categories during the financial crisis by firms exposed to credit market frictions are less successful throughout their entire life cycle than products introduced in new categories by the same firm during normal times. Overall, our findings suggest that disrupted credit markets disrupt radical product innovation.
Relative Sentiment and Machine Learning for Tactical Asset Allocation
SSRN
We examine Sentix sentiment indices for use in tactical asset allocation. In particular, we construct monthly relative sentiment factors for the U.S., Europe, Japan, and Asia ex-Japan by taking the difference in 6-month economic expectations between each region's institutional and individual investors. These factors (along with one-month forward equity returns) then serve as inputs to a wide array of machine learning algorithms. Employing combinatorial cross-validation and adjusting for data snooping, we find relative sentiment factors have robust and significant predictive power in all four regions; that they surpass both standalone sentiment and time-series momentum in terms of informational content; and that they demonstrate the ability to identify the subsequent best- and worst-performing global equity markets from along a cross-section. The results are consistent with previous findings on relative sentiment, discovered using unrelated datasets.
SSRN
We examine Sentix sentiment indices for use in tactical asset allocation. In particular, we construct monthly relative sentiment factors for the U.S., Europe, Japan, and Asia ex-Japan by taking the difference in 6-month economic expectations between each region's institutional and individual investors. These factors (along with one-month forward equity returns) then serve as inputs to a wide array of machine learning algorithms. Employing combinatorial cross-validation and adjusting for data snooping, we find relative sentiment factors have robust and significant predictive power in all four regions; that they surpass both standalone sentiment and time-series momentum in terms of informational content; and that they demonstrate the ability to identify the subsequent best- and worst-performing global equity markets from along a cross-section. The results are consistent with previous findings on relative sentiment, discovered using unrelated datasets.
Residence and Citizenship by Investment: An Updated Database on Immigrant Investor Programs
SSRN
In the last decades, many countries have chosen to implement Immigrant Investor Programs (IIPs) to attract foreign capital and boost national economies. IIPs are based on a conditional exchange logic according to which the host country offers residence permits (Residence by Investment â€" RBI â€"), citizenship (Citizenship by Investment â€" CBI â€") and, sometimes, preferential tax regimes, to third-country nationals making substantial investments within its territory. The influence of this kind of economic strategies on in â€" and out â€" migration of individuals and capitals leads the polarization of both immigrants and states. IIPs, in fact, tend to reveal the existence of a lucky class of immigrants, able to invest in order to skip the standard procedures for obtaining residence permits and/or citizenship, and to show that the provision of preferential treatments is apt to turn states into “top destinationsâ€. Such a polarization does not arise from the willingness to damage a group of immigrants or from attempts to undermine the economy of other countries; by implementing IIPs, host countries pursue economic benefits without minding on any detrimental consequence. IIPs can generate discrimination between high and low-income people â€" immigrants and citizens â€", unfair economic competition among countries, and also mismatches in the field of taxation. All these negative impacts have been widely addressed in recent publications, but they still exist and to fight them, it is important to keep the information related to RBIs and the CBIs always updated. This paper provides a database of these programs and their main characteristics with the aim to facilitate the work of all those researchers and organizations that commit themselves to the study of the factors that give rise to economic discrimination and unfair competition.
SSRN
In the last decades, many countries have chosen to implement Immigrant Investor Programs (IIPs) to attract foreign capital and boost national economies. IIPs are based on a conditional exchange logic according to which the host country offers residence permits (Residence by Investment â€" RBI â€"), citizenship (Citizenship by Investment â€" CBI â€") and, sometimes, preferential tax regimes, to third-country nationals making substantial investments within its territory. The influence of this kind of economic strategies on in â€" and out â€" migration of individuals and capitals leads the polarization of both immigrants and states. IIPs, in fact, tend to reveal the existence of a lucky class of immigrants, able to invest in order to skip the standard procedures for obtaining residence permits and/or citizenship, and to show that the provision of preferential treatments is apt to turn states into “top destinationsâ€. Such a polarization does not arise from the willingness to damage a group of immigrants or from attempts to undermine the economy of other countries; by implementing IIPs, host countries pursue economic benefits without minding on any detrimental consequence. IIPs can generate discrimination between high and low-income people â€" immigrants and citizens â€", unfair economic competition among countries, and also mismatches in the field of taxation. All these negative impacts have been widely addressed in recent publications, but they still exist and to fight them, it is important to keep the information related to RBIs and the CBIs always updated. This paper provides a database of these programs and their main characteristics with the aim to facilitate the work of all those researchers and organizations that commit themselves to the study of the factors that give rise to economic discrimination and unfair competition.
Terror Attacks and Cryptocurrency Trading Volume
SSRN
This paper investigates whether the change of crypto-trading volume can affect the occurrence of terror attacks. We find that an increase in the Bitcoin and Ethereum trading volume is related to the higher probability of the occurrence of terror attacks while other main cryptocurrencies have no such effects. Furthermore, the effect of Bitcoin volume on terror attacks only exists in the United Kingdom, rather than the United States, Germany, and France. Finally, the empirical results reveal that the more media attention on cryptocurrency would weaken the effect of crypto-trading volume on the occurrence of terror attacks.
SSRN
This paper investigates whether the change of crypto-trading volume can affect the occurrence of terror attacks. We find that an increase in the Bitcoin and Ethereum trading volume is related to the higher probability of the occurrence of terror attacks while other main cryptocurrencies have no such effects. Furthermore, the effect of Bitcoin volume on terror attacks only exists in the United Kingdom, rather than the United States, Germany, and France. Finally, the empirical results reveal that the more media attention on cryptocurrency would weaken the effect of crypto-trading volume on the occurrence of terror attacks.
The Brexit Reform of European Financial Supervision: Lost in Transition?
SSRN
This article reviews the impact of Brexit on the European System of Financial Supervision (ESFS) and claims that the withdrawal of the United Kingdom will lead to more centralisation of supervision at EU level and a tightening of the supervision over third countries’ markets actors. This conclusion is based on a study of both the European authorities’ activism and the amendments recently adopted by EU legislators to reform the functioning of European financial supervision. Brexit has highlighted the extent of the remaining gaps in European financial supervision, which are partly due to the role that the UK played in designing European financial supervision. The reform is also geared towards mitigating the “costs†for the European economy incurred by Brexit. By analysing these developments, this contribution argues that Brexit has been seized by the European Union as an opportunity to significantly strengthen European financial supervision.
SSRN
This article reviews the impact of Brexit on the European System of Financial Supervision (ESFS) and claims that the withdrawal of the United Kingdom will lead to more centralisation of supervision at EU level and a tightening of the supervision over third countries’ markets actors. This conclusion is based on a study of both the European authorities’ activism and the amendments recently adopted by EU legislators to reform the functioning of European financial supervision. Brexit has highlighted the extent of the remaining gaps in European financial supervision, which are partly due to the role that the UK played in designing European financial supervision. The reform is also geared towards mitigating the “costs†for the European economy incurred by Brexit. By analysing these developments, this contribution argues that Brexit has been seized by the European Union as an opportunity to significantly strengthen European financial supervision.
The Economic Impact of Distributing Financial Products on Third-Party Online Platforms
SSRN
The emergence of third-party online platforms in inter-mediating financial products has been a new and exciting development in FinTech. In China, the platforms are allowed to distribute mutual funds since 2012, and have quickly grown into a formidable presence. Examining the economic impact of this new distributional channel, we use the staggered entrance of mutual funds onto the platforms to identify the casual effect of online platforms on the behaviors of fund investors and fund managers. We find that, post-platform, fund flows become markedly more sensitive to fund performance. The net flow to the top 10% performing funds more than triples their pre-platform level, and this pattern of increased performance sensitivity is further confirmed using private data from Howbuy, a top-five platform in China. Consistent with the added incentive of becoming a top ranking performer in the era of large-scale platforms, we find that fund managers increase their risk taking to enhance the probability of getting into the top rank. Meanwhile, the organization structure of large fund families weakens as the introduction of platforms levels the playing field for all funds.
SSRN
The emergence of third-party online platforms in inter-mediating financial products has been a new and exciting development in FinTech. In China, the platforms are allowed to distribute mutual funds since 2012, and have quickly grown into a formidable presence. Examining the economic impact of this new distributional channel, we use the staggered entrance of mutual funds onto the platforms to identify the casual effect of online platforms on the behaviors of fund investors and fund managers. We find that, post-platform, fund flows become markedly more sensitive to fund performance. The net flow to the top 10% performing funds more than triples their pre-platform level, and this pattern of increased performance sensitivity is further confirmed using private data from Howbuy, a top-five platform in China. Consistent with the added incentive of becoming a top ranking performer in the era of large-scale platforms, we find that fund managers increase their risk taking to enhance the probability of getting into the top rank. Meanwhile, the organization structure of large fund families weakens as the introduction of platforms levels the playing field for all funds.
The Impact of Oil Price Uncertainty on GCC Stock Markets
SSRN
This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models. Empirical results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock returns. The GCC stock returns are found to be negatively affected by oil market uncertainty for almost the entire period under examination. More interestingly, we find that the impact of oil price uncertainty differs across GCC member states and allow for grouping. The results also show that the stock markets of Oman and Bahrain are relatively less sensitive to the oil uncertainty factor, thus offering investors and portfolio managers different investment options andportfolio diversification opportunities across GCC members.
SSRN
This paper investigates the dynamics of the co-movement of GCC stock market returns with global oil market uncertainty, using an ARMA-DCC-EGARCH and time varying Student-t copula models. Empirical results demonstrate that oil uncertainty has significant and time varying impacts on the GCC stock returns. The GCC stock returns are found to be negatively affected by oil market uncertainty for almost the entire period under examination. More interestingly, we find that the impact of oil price uncertainty differs across GCC member states and allow for grouping. The results also show that the stock markets of Oman and Bahrain are relatively less sensitive to the oil uncertainty factor, thus offering investors and portfolio managers different investment options andportfolio diversification opportunities across GCC members.
